Causal Mechanism-based Model Construction

01/16/2013
by   Tsai-Ching Lu, et al.
0

We propose a framework for building graphical causal model that is based on the concept of causal mechanisms. Causal models are intuitive for human users and, more importantly, support the prediction of the effect of manipulation. We describe an implementation of the proposed framework as an interactive model construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and Learning Engine) and in GeNIe (SMILE's Windows user interface).

READ FULL TEXT

page 1

page 7

page 8

research
09/03/2017

Estimation of interventional effects of features on prediction

The interpretability of prediction mechanisms with respect to the underl...
research
06/14/2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

We introduce DoWhy-GCM, an extension of the DoWhy Python library, that l...
research
11/20/2021

Building Object-based Causal Programs for Human-like Generalization

We present a novel task that measures how people generalize objects' cau...
research
09/08/2022

Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface

Strong untestable assumptions are almost universal in causal point estim...
research
07/18/2020

Structure Mapping for Transferability of Causal Models

Human beings learn causal models and constantly use them to transfer kno...
research
05/07/2020

Expressing Accountability Patterns using Structural Causal Models

While the exact definition and implementation of accountability depend o...
research
01/26/2017

The Causal Frame Problem: An Algorithmic Perspective

The Frame Problem (FP) is a puzzle in philosophy of mind and epistemolog...

Please sign up or login with your details

Forgot password? Click here to reset